Virtual traffic simulation with neural network learned mobility model. (January 2018) - Zhang, Jian; El Kamel, Abdelkader
Highlights: A mobility model is proposed for large scale virtual traffic flow simulation. The model is learned by using neural networks on highway traffic data. Detailed trajectory is under consideration in this model. The result is demonstrated by using SUMO simulator. Providing a platform for further research on traffic organization. Abstract: Virtual traffic simulation plays an important role in easing traffic congestion and reducing traffic pollution. As the transportation network expands, the former rule-based mobility models showed several limitations in producing convincing virtual vehicles. A more realistic model with example-based method is in demand. In this paper, a neural network is employed with carefully selected traffic trajectory data. The virtual vehicle production is driven by the proposed mobility model and organized by a specified structure. Then, the virtual traffic simulation could be given for an indicated scenario.